Merge branch 'main' into miniforge

.github/workflows/tests.yml

@@ -40,8 +40,7 @@ jobs:
   pytest:
     name: '${{ matrix.os }} / ${{ matrix.kind }} / ${{ matrix.python }}'
     needs: style
-    timeout-minutes: 70
-    continue-on-error: true
+    timeout-minutes: 80
     runs-on: ${{ matrix.os }}
     defaults:
       run:
@@ -54,6 +53,7 @@ jobs:
       MNE_CI_KIND: '${{ matrix.kind }}'
       CI_OS_NAME: '${{ matrix.os }}'
     strategy:
+      fail-fast: false
       matrix:
         include:
           - os: ubuntu-latest
@@ -132,4 +132,4 @@ jobs:
       - uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-        if: always()
+        if: success() || failure()

doc/changes/devel/12924.bugfix.rst

@@ -0,0 +1 @@
+Fix typos in the Spatio-Spectral Decomposition example, by :newcontrib:`Simon M. Hofmann`.

doc/changes/names.inc

@@ -275,6 +275,7 @@
 .. _Simeon Wong: https://github.com/dtxe
 .. _Simon Kern: https://skjerns.de
 .. _Simon Kornblith: https://simonster.com
+.. _Simon M. Hofmann: https://github.com/SHEscher
 .. _Sondre Foslien: https://github.com/sondrfos
 .. _Sophie Herbst: https://github.com/SophieHerbst
 .. _Stanislas Chambon: https://github.com/Slasnista

doc/sphinxext/mne_doc_utils.py

@@ -95,6 +95,8 @@ def reset_warnings(gallery_conf, fname):
         r"numpy\.core is deprecated and has been renamed to numpy\._core",
         # matplotlib
         "__array_wrap__ must accept context and return_scalar.*",
+        # selenium
+        "setting remote_server_addr",
     ):
         warnings.filterwarnings(  # deal with other modules having bad imports
             "ignore", message=f".*{key}.*", category=DeprecationWarning

examples/decoding/ssd_spatial_filters.py

@@ -1,9 +1,9 @@
 """
 .. _ex-ssd-spatial-filters:
 
-===========================================================
-Compute Spectro-Spatial Decomposition (SSD) spatial filters
-===========================================================
+================================================================
+Compute spatial filters with Spatio-Spectral Decomposition (SSD)
+================================================================
 
 In this example, we will compute spatial filters for retaining
 oscillatory brain activity and down-weighting 1/f background signals
@@ -33,7 +33,7 @@
 
 # Prepare data
 raw = mne.io.read_raw_ctf(fname)
-raw.crop(50.0, 110.0).load_data()  # crop for memory purposes
+raw.crop(tmin=50.0, tmax=110.0).load_data()  # crop for memory purposes
 raw.resample(sfreq=250)
 
 raw.pick_types(meg=True, ref_meg=False)
@@ -63,9 +63,9 @@
 
 
 # %%
-# Let's investigate spatial filter with max power ratio.
+# Let's investigate spatial filter with the max power ratio.
 # We will first inspect the topographies.
-# According to Nikulin et al. 2011 this is done by either inverting the filters
+# According to Nikulin et al. (2011), this is done by either inverting the filters
 # (W^{-1}) or by multiplying the noise cov with the filters Eq. (22) (C_n W)^t.
 # We rely on the inversion approach here.
 
@@ -86,7 +86,7 @@
 # Note that this is not necessary if sort_by_spectral_ratio=True (default).
 spec_ratio, sorter = ssd.get_spectral_ratio(ssd_sources)
 
-# Plot spectral ratio (see Eq. 24 in Nikulin 2011).
+# Plot spectral ratio (see Eq. 24 in Nikulin et al., 2011).
 fig, ax = plt.subplots(1)
 ax.plot(spec_ratio, color="black")
 ax.plot(spec_ratio[sorter], color="orange", label="sorted eigenvalues")
@@ -100,7 +100,7 @@
 # the sorting might make a difference.
 
 # %%
-# Let's also look at the power spectrum of that source and compare it to
+# Let's also look at the power spectrum of that source and compare it
 # to the power spectrum of the source with lowest SNR.
 
 below50 = freqs < 50
@@ -121,7 +121,7 @@
 # %%
 # Epoched data
 # ------------
-# Although we suggest to use this method before epoching, there might be some
+# Although we suggest using this method before epoching, there might be some
 # situations in which data can only be treated by chunks.
 
 # Build epochs as sliding windows over the continuous raw file.

mne/conftest.py

@@ -139,6 +139,7 @@ def pytest_configure(config):
     ignore:Passing a schema to Validator\.iter_errors is deprecated.*:
     ignore:Unclosed context <zmq.asyncio.Context.*:ResourceWarning
     ignore:Jupyter is migrating its paths.*:DeprecationWarning
+    ignore:datetime\.datetime\.utcnow\(\) is deprecated.*:DeprecationWarning
     ignore:Widget\..* is deprecated\.:DeprecationWarning
     ignore:.*is deprecated in pyzmq.*:DeprecationWarning
     ignore:The `ipykernel.comm.Comm` class has been deprecated.*:DeprecationWarning

mne/decoding/receptive_field.py

@@ -392,7 +392,7 @@ def _check_dimensions(self, X, y, predict=False):
                     f"X and y do not have the same n_epochs\n{X.shape[1]} != "
                     f"{y.shape[1]}"
                 )
-            if predict and y.shape[-1] != len(self.estimator_.coef_):
+            if predict and y.shape[-1] not in (len(self.estimator_.coef_), 1):
                 raise ValueError(
                     "Number of outputs does not match estimator coefficients dimensions"
                 )

mne/decoding/tests/test_receptive_field.py

@@ -198,6 +198,7 @@ def test_receptive_field_basic(n_jobs):
     feature_names = [f"feature_{ii}" for ii in [0, 1, 2]]
     rf = ReceptiveField(tmin, tmax, 1, feature_names, estimator=mod, patterns=True)
     rf.fit(X, y)
+    assert rf.coef_.shape == (3, 11)
     assert_array_equal(rf.delays_, np.arange(tmin, tmax + 1))
 
     y_pred = rf.predict(X)

mne/stats/regression.py

@@ -297,9 +297,10 @@ def solver(X, y):
     coefs = solver(X, data.T)
     if coefs.shape[0] != data.shape[0]:
         raise ValueError(
-            "solver output has unexcepted shape. Supply a "
+            f"solver output has unexcepted shape {coefs.shape}. Supply a "
             "function that returns coefficients in the form "
-            "(n_targets, n_features), where targets == channels."
+            "(n_targets, n_features), where "
+            f"n_targets == n_channels == {data.shape[0]}."
         )
 
     # construct Evoked objects to be returned from output

mne/stats/tests/test_regression.py

@@ -139,7 +139,9 @@ def test_continuous_regression_with_overlap():
     from sklearn.linear_model import ridge_regression
 
     def solver(X, y):
-        return ridge_regression(X, y, alpha=0.0, solver="cholesky")
+        # Newer scikit-learn returns 1D array for ridge_regression, so ensure
+        # 2D output
+        return np.atleast_2d(ridge_regression(X, y, alpha=0.0, solver="cholesky"))
 
     assert_allclose(
         effect,